'''simple docstring'''




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class        a__ (  unittest.TestCase					):
     lowerCamelCase    :	Any				=MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     lowerCamelCase    :	Any				=TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						List[str]    ,							a						:						List[Any]    ,							a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    AudioClassificationPipeline(model=a    ,							feature_extractor=a       )

         # test with a raw waveform
         __lowerCamelCase            =    np.zeros((3_40_00,)       )
         __lowerCamelCase            =    np.zeros((1_40_00,)       )
         return audio_classifier, [audioa, audio]






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						int    ,							a						:						int       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    examples
         __lowerCamelCase            =    audio_classifier(a       )
         # by default a model is initialized with num_labels=2
         self.assertEqual(
             a    ,							[
                 {'''score''': ANY(a       ), '''label''': ANY(a       )},
                 {'''score''': ANY(a       ), '''label''': ANY(a       )},
             ]    ,							)
         __lowerCamelCase            =    audio_classifier(a    ,							top_k=1       )
         self.assertEqual(
             a    ,							[
                 {'''score''': ANY(a       ), '''label''': ANY(a       )},
             ]    ,							)

         self.run_torchaudio(a       )






     @require_torchaudio
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						Dict       ):





         """simple docstring"""



         import datasets

         # test with a local file
         __lowerCamelCase            =    datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy'''    ,							'''clean'''    ,							split='''validation'''       )
         __lowerCamelCase            =    dataset[0]['''audio''']['''array''']
         __lowerCamelCase            =    audio_classifier(a       )
         self.assertEqual(
             a    ,							[
                 {'''score''': ANY(a       ), '''label''': ANY(a       )},
                 {'''score''': ANY(a       ), '''label''': ANY(a       )},
             ]    ,							)






     @require_torch
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''anton-l/wav2vec2-random-tiny-classifier'''

         __lowerCamelCase            =    pipeline('''audio-classification'''    ,							model=a       )

         __lowerCamelCase            =    np.ones((80_00,)       )
         __lowerCamelCase            =    audio_classifier(a    ,							top_k=4       )

         __lowerCamelCase            =    [
             {'''score''': 0.08_42, '''label''': '''no'''},
             {'''score''': 0.08_38, '''label''': '''up'''},
             {'''score''': 0.08_37, '''label''': '''go'''},
             {'''score''': 0.08_34, '''label''': '''right'''},
         ]
         __lowerCamelCase            =    [
             {'''score''': 0.08_45, '''label''': '''stop'''},
             {'''score''': 0.08_44, '''label''': '''on'''},
             {'''score''': 0.08_41, '''label''': '''right'''},
             {'''score''': 0.08_34, '''label''': '''left'''},
         ]
         self.assertIn(nested_simplify(a    ,							decimals=4       )    ,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]       )

         __lowerCamelCase            =    {'''array''': np.ones((80_00,)       ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
         __lowerCamelCase            =    audio_classifier(a    ,							top_k=4       )
         self.assertIn(nested_simplify(a    ,							decimals=4       )    ,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]       )






     @require_torch
     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         import datasets

         __lowerCamelCase            =    '''superb/wav2vec2-base-superb-ks'''

         __lowerCamelCase            =    pipeline('''audio-classification'''    ,							model=a       )
         __lowerCamelCase            =    datasets.load_dataset('''anton-l/superb_dummy'''    ,							'''ks'''    ,							split='''test'''       )

         __lowerCamelCase            =    np.array(dataset[3]['''speech''']    ,							dtype=np.floataa       )
         __lowerCamelCase            =    audio_classifier(a    ,							top_k=4       )
         self.assertEqual(
             nested_simplify(a    ,							decimals=3       )    ,							[
                 {'''score''': 0.9_81, '''label''': '''go'''},
                 {'''score''': 0.0_07, '''label''': '''up'''},
                 {'''score''': 0.0_06, '''label''': '''_unknown_'''},
                 {'''score''': 0.0_01, '''label''': '''down'''},
             ]    ,							)






     @require_tf
     @unittest.skip('''Audio classification is not implemented for TF'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         pass

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
